TY - JOUR
AU - Jiu, Zhiqing
AB - In recent years, U-Net and its extended variants have made remarkable progress in the realm of liver and liver tumor segmentation. However, the limitations of single-path convolutional operations have hindered the full exploitation of valuable features and restricted their mobility within networks. Moreover, the semantic gap between shallow and deep features proves that a simplistic shortcut is not enough. To address these issues and realize automatic liver and tumor area segmentation in CT images, we introduced the multi-scale feature fusion with dense connections and an attention mechanism segmentation method (MDAU-Net). This network leverages the multi-head attention (MHA) mechanism and multi-scale feature fusion. First, we introduced a double-flow linear pooling enhancement unit to optimize the fusion of deep and shallow features while mitigating the semantic gap between them. Subsequently, we proposed a cascaded adaptive feature extraction unit, combining attention mechanisms with a series of dense connections to capture valuable information and encourage feature reuse. Additionally, we designed a cross-level information interaction mechanism utilizing bidirectional residual connections to address the issue of forgetting a priori knowledge during training. Finally, we assessed MDAU-Net’s performance on the LiTS and SLiver07 datasets. The experimental results demonstrated that MDAU-Net is well-suited for liver and tumor segmentation tasks, outperforming existing widely used methods in terms of robustness and accuracy.
TI - MDAU-Net: A Liver and Liver Tumor Segmentation Method Combining an Attention Mechanism and Multi-Scale Features
JF - Applied Sciences
DO - 10.3390/app131810443
DA - 2023-09-18
UR - https://www.deepdyve.com/lp/multidisciplinary-digital-publishing-institute/mdau-net-a-liver-and-liver-tumor-segmentation-method-combining-an-Ev6blZ604Z
SP - 10443
VL - 13
IS - 18
DP - DeepDyve
ER -